期刊
NUCLEUS
卷 7, 期 1, 页码 68-83出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/19491034.2016.1149664
关键词
breast cancer; LINC complex; lamin A; C; nuclear morphology; nuclear envelope; SUN1; SUN2; wndchrm
类别
资金
- Joint Usage/Research Center for Developmental Medicine, Institute of Molecular Embryology and Genetics, Kumamoto University
- Japan Society for the Promotion of Science
- Ministry of Education, Culture, Sports, Science and Technology of Japan
- Grants-in-Aid for Scientific Research [25116009, 14J01290, 15H04707] Funding Source: KAKEN
A supervised machine learning algorithm, which is qualified for image classification and analyzing similarities, is based on multiple discriminative morphological features that are automatically assembled during the learning processes. The algorithm is suitable for population-based analysis of images of biological materials that are generally complex and heterogeneous. Here we used the algorithm wndchrm to quantify the effects on nucleolar morphology of the loss of the components of nuclear envelope in a human mammary epithelial cell line. The linker of nucleoskeleton and cytoskeleton (LINC) complex, an assembly of nuclear envelope proteins comprising mainly members of the SUN and nesprin families, connects the nuclear lamina and cytoskeletal filaments. The components of the LINC complex are markedly deficient in breast cancer tissues. We found that a reduction in the levels of SUN1, SUN2, and lamin A/C led to significant changes in morphologies that were computationally classified using wndchrm with approximately 100% accuracy. In particular, depletion of SUN1 caused nucleolar hypertrophy and reduced rRNA synthesis. Further, wndchrm revealed a consistent negative correlation between SUN1 expression and the size of nucleoli in human breast cancer tissues. Our unbiased morphological quantitation strategies using wndchrm revealed an unexpected link between the components of the LINC complex and the morphologies of nucleoli that serves as an indicator of the malignant phenotype of breast cancer cells.
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